20. • Structured Learning Approach
• bestlink SVM:
– A linear model parameterized by
space of task partitions space of best-links feature vector
q1 q2 q3 q4 q6q5q0
Learning to Extract Cross-Session Tasks
[Wang et al. WWW’13]
22. Query similarity computation
• Query-based features (9)
– Query term cosine similarity
– Query string edit distance
• URL-based features (14)
– Jaccard coefficient between clicked URL sets
– Average ODP category similarity
• Session-based features (3)
– Same session
– # of sessions in between
q1 q2 q3q0
Learning to Extract Cross-Session Tasks
[Wang et al. WWW’13]
23. • Solving the bestlink SVM
• Optimizing latent structure SVMs
Margin
# queries # annotated tasks (dis)agreement on
the best links
Solver: [Chang et al. ICML’10]
23
Learning to Extract Cross-Session Tasks
[Wang et al. WWW’13]
40. Task: plan a wedding
– Sample queries:
• wedding planning
• wedding checklist
• bridal dresses
• wedding cards
– Classify each word as background word or subtask-specific
word
– Leverage word embeddings
• Use a weighted combination of their embedding vectors to encode a
query's vector:
Quantifying Task based Distances
49. • Build upon Bayesian Rose Trees
– Each node of the tree corresponds to a task
– Each task represented by a set of queries
• Goal: Find the tree structure that maximizes
• Number of partitions consistent with T can be exponentially large
– Approximate using dynamic programming:
åÎ
=
)()(
))(|())(()|(
TPartT
TQpTpTQp
f
ff
Hierarchical Task Extraction
Likelihood of queries
belong to same task
)|)(()1()()|(
)(
ii
TchT
TTT TTleavespQfTQP
i
ÕÎ
-+= pp
Mixture over
partitions of
data points
58. Intent Understanding in Personal Assistants
• Intent ßà Context
• Contextual Signals:
– External: physical environment,
e.g. location, time
– Internal: user’s activities, e.g. apps, venues
• Intent & Contextual examples:
– To listen to music ---- driving or using browsers
– To check calendar ---- Sunday evening or at office
• Track User’s Intent:
– What users intend to know: informational intent
– What users intend to do: task-completion intent
Contextual Intent Tracking for Personal Assistants; KDD 2016
59. Intent Understanding in Personal Assistants
• Given:
– A set of users, tracking granularity
– Type of intent, context of user
• The intent tracking problem is to
determine:
– Whether user u has intent I
– For every time step of length delta
• Adopt Parafac2 tensor decomposition
– PARAFAC2 decomposition fails to model sequential correlations within panels
– Latent factors and contextual signals jointly modeled using Kalman filters:
Contextual Intent Tracking for Personal Assistants; KDD 2016